Income Inequality across the Rural-Urban Continuum in the United States, 1970-2016

Examining area- and individual-level differences in suicide ideation severity and suicide attempt among youth

Youth suicide is a pressing problem and suicide rates are not equally distributed across geographic areas or socioeconomic status (SES). Death by suicide is often preceded factors including hopelessness and suicide ideation, planning, and attempt. The current study examined area- and individual-level differences in suicide ideation severity and suicide attempt in a state-representative sample of youth from 2019 (N = 78,740) and 2021 (N = 61,396). Youth from higher SES and rural areas showed lower suicide ideation severity and odds of suicide attempt. After including individual-level covariates, SES differences in ideation severity and suicide attempt persisted for 2019 but not 2021. Rural differences for ideation severity persisted across years but not for suicide attempt. Further research on geographic variation in suicide risk is needed.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Contextualizing physical activity in rural adults: Do relationships between income inequality, neighborhood environments, and physical activity exist?

OBJECTIVE

To examine if income inequality, social cohesion, and neighborhood walkability are associated with physical activity among rural adults.

DATA SOURCE

Cross-sectional data came from a telephone survey (August 2020-March 2021) that examined food access, physical activity, and neighborhood environments across rural counties in a southeastern state.

STUDY DESIGN

Multinomial logistic regression models assessed the likelihood of being active versus inactive and insufficiently active versus inactive in this rural population. Coefficients are presented as relative risk ratios (RRRs). Statistical significance was determined using 95% confidence intervals (CIs). All analyses were performed in STATA 16.1.

DATA COLLECTION/EXTRACTION METHODS

Trained university students administered the survey. Students verbally obtained consent, read survey items, and recorded responses into Qualtrics software. Upon survey completion, respondents were mailed a $10 incentive card and printed informed consent form. Eligible participants were ≥18 years old and current residents of included counties.

PRINCIPAL FINDINGS

Respondents in neighborhoods with relatively high social cohesion versus low social cohesion were more likely to be active than inactive (RRR = 2.50, 95% CI: 1.27-4.90, p < 0.01), after accounting for all other variables in the model. Income inequality and neighborhood walkability were not associated with different levels of physical activity in the rural sample.

CONCLUSIONS

Study findings contribute to limited knowledge on the relationship between neighborhood environmental contexts and physical activity among rural populations. The health effects of neighborhood social cohesion warrant more attention in health equity research and consideration when developing multilevel interventions to improve the health of rural populations.

Productivity costs associated with reactive school closures related to influenza or influenza-like illness in the United States from 2011 to 2019

INTRODUCTION

Schools close in reaction to seasonal influenza outbreaks and, on occasion, pandemic influenza. The unintended costs of reactive school closures associated with influenza or influenza-like illness (ILI) has not been studied previously. We estimated the costs of ILI-related reactive school closures in the United States over eight academic years.

METHODS

We used prospectively collected data on ILI-related reactive school closures from August 1, 2011 to June 30, 2019 to estimate the costs of the closures, which included productivity costs for parents, teachers, and non-teaching school staff. Productivity cost estimates were evaluated by multiplying the number of days for each closure by the state- and year-specific average hourly or daily wage rates for parents, teachers, and school staff. We subdivided total cost and cost per student estimates by school year, state, and urbanicity of school location.

RESULTS

The estimated productivity cost of the closures was $476 million in total during the eight years, with most (90%) of the costs occurring between 2016-2017 and 2018-2019, and in Tennessee (55%) and Kentucky (21%). Among all U.S. public schools, the annual cost per student was much higher in Tennessee ($33) and Kentucky ($19) than any other state ($2.4 in the third highest state) or the national average ($1.2). The cost per student was higher in rural areas ($2.9) or towns ($2.5) than cities ($0.6) or suburbs ($0.5). Locations with higher costs tended to have both more closures and closures with longer durations.

CONCLUSIONS

In recent years, we found significant heterogeneity in year-to-year costs of ILI-associated reactive school closures. These costs have been greatest in Tennessee and Kentucky and been elevated in rural or town areas relative to cities or suburbs. Our findings might provide evidence to support efforts to reduce the burden of seasonal influenza in these disproportionately impacted states or communities.

Explaining obesity disparities by urbanicity, 2006 to 2016: A decomposition analysis

OBJECTIVE

A large, and potentially growing, disparity in obesity prevalence exists between large central metros and less urban United States counties. This study examines its key predictors.

METHODS

Using a rich county-year data set spanning 2006 to 2016, the authors conducted a Gelbach decomposition to examine the relative importance of demographic, socioeconomic, environmental, and behavioral factors in shaping the baseline obesity gap and the growth rate over time between large central metros and other counties.

RESULTS

Predictors included in this model explain almost the entire obesity gap between large central metros and other counties in the baseline year but can explain only ~32% of the growing gap. At baseline, demographic predictors explain more than half the obesity gap, and socioeconomic and behavioral predictors explain the other half. Behavioral and socioeconomic predictors explain more than half the growing gap over time whereas controlling for environmental and demographic predictors decreases the obesity gap by urbanicity over time.

CONCLUSIONS

Results suggest policy makers should prioritize interventions targeting health behaviors of residents in non-large central metros to slow the growth of the obesity gap between large central metros and other counties. However, to fundamentally eliminate the obesity gap, in addition to improving health behaviors, policies addressing socioeconomic inequalities are needed.

Rural-Urban disparities in self-reported physical/mental multimorbidity: A cross-sectional study of self-reported mental health and physical health among working age adults in the U.S

Purpose

Self-rated physical health (SRPH) and self-rated mental health (SRMH) are both linked to excess morbidity and premature mortality and can vary across rural and urban contexts. This can be particularly problematic for rural residents who have less access to important health care infrastructure. In this paper, we assess the prevalence of and rural-urban disparities at the intersection of SRPH and SRMH, specifically self-rated physical/mental multimorbidity (SRPMM) overall and across rural-urban contexts.

Methods

Using a cross-sectional demographically representative national dataset of over 4000 working age adults in the U.S., we expose rural-urban differences in the prevalence of SRPMM and explore individual-level factors that may explain this disparity.

Results

Approximately 15 percent of working age adults reported SRPMM, but rural adults were at higher risk than their urban counterparts. However, this disadvantage disappeared for remote rural working-age adults and was attenuated for metro-adjacent rural working-age adults when we controlled for the fact that rural adults had lower household incomes.

Conclusion

Findings reveal a higher risk of SRPMM among rural adults, in part because of lower incomes among this group. This work acts as the foundation for facilitating research on and addressing rural-urban disparities in SRPMM.

Hardship in the Heartland: Associations Between Rurality, Income, and Material Hardship

One in three U.S. households has experienced material hardship. The inadequate provision of basic needs, including food, healthcare, and transportation, is more typical in households with children or persons of color, yet little is known about material hardship in rural spaces. The aim of this study is to describe the prevalence of material hardships in Iowa and examine the relationship between rurality, income, and material hardship. Using data from the 2016 State Innovation Model Statewide Consumer Survey, we use logistic regression to examine the association between rurality, income, and four forms of material hardship. Rural respondents incurred lower odds than non-rural respondents for all four hardship models. All four models indicated that lower income respondents incurred greater odds for having material hardship. Material hardship was reported across all groups, with rurality, income, race, and age as strong predictors of material hardship among our sample.

Income inequality and child maltreatment rates in US counties, 2009-2018

BACKGROUND AND OBJECTIVE

Studies have confirmed that income inequality is associated with compromised health and well-being. However, much less is known about the effects of county-level income inequality on risk of perpetrating child maltreatment, particularly for distinct types of child maltreatment. By utilizing recent national data over 10 years (2009-2018), our study explored the associations of county-level income inequality (i.e., Gini index and income quantile ratios) with child maltreatment rates, including both overall and specific types of maltreatment rates (i.e., physical, psychological, and sexual abuse and neglect) in the US.

PARTICIPANTS AND SETTING

We utilized data from approximately 902 US counties by linking the National Child Abuse and Neglect Data System with the American Community Survey.

METHODS

Ordinary Least Squares regression models were estimated to examine the relationship between county-level income inequality and child maltreatment rates and the moderating role of poverty rates.

RESULTS

Higher scores on county-level Gini index were significantly associated with higher overall child maltreatment rates and neglect, after controlling for county-level characteristics. Income quantile ratios were significantly associated with overall child maltreatment, physical abuse, and neglect. We also found significant interaction effects between income inequality and poverty rates in the associations with physical and psychological abuse rates, suggesting that the effects of inequality were exacerbated by county-level poverty.

CONCLUSIONS

Given the tremendous increases in inequality in the US over recent decades, this research sheds light on the mechanisms through which inequality impacts parents' caregiving abilities in highly unequal counties.

Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China

The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper.

Can Digital Inclusive Finance Narrow the Chinese Urban–Rural Income Gap? The Perspective of the Regional Urban–Rural Income Structure

This paper empirically studies the impact of digital inclusive finance on the income structure of urban and rural residents in eastern, central, and western China. The results show that, first, digital inclusive finance is beneficial to narrowing the urban–rural per capita disposable income gap that has a disequilibrium effect among regions. Second, narrowing the wage income, property income, and transfer income gaps is beneficial but has little effect on the net operating income gap between urban and rural residents. Third, narrowing the wage income, property income, and transfer income gaps reduces the total income gap, and the wage income gap has the strongest intermediary force. In the end, the paper puts forward corresponding countermeasures for the development of digital inclusive finance to narrow each of these income gaps in different regions of China.